{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## _*LiH dissociation curve using VQE with UCCSD variational form*_\n",
    "\n",
    "This notebook demonstrates using Qiskit Chemistry to plot graphs of the ground state energy of the Lithium Hydride (LiH) molecule over a range of inter-atomic distances using VQE and UCCSD. It is compared to the same energies as computed by the NumPyMinimumEigensolver\n",
    "\n",
    "This notebook has been written to use the PYSCF chemistry driver."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing step 22 --- complete\n",
      "Distances:  [0.6  0.7  0.8  0.9  1.   1.1  1.2  1.3  1.4  1.5  1.6  1.7  1.8  1.9\n",
      " 2.   2.25 2.5  2.75 3.   3.25 3.5  3.75 4.  ]\n",
      "Energies: [[-7.3133458  -7.50092206 -7.63097823 -7.7208124  -7.78224239 -7.82359927\n",
      "  -7.85069837 -7.86756328 -7.87700148 -7.8810157  -7.88107203 -7.87826815\n",
      "  -7.87344011 -7.86723367 -7.86015319 -7.84104235 -7.82307636 -7.80861236\n",
      "  -7.79836328 -7.79175303 -7.78771683 -7.7853196  -7.78391829]\n",
      " [-7.31334583 -7.50092209 -7.63097825 -7.72081241 -7.7822424  -7.82359928\n",
      "  -7.85069838 -7.86756329 -7.87700149 -7.88101572 -7.88107204 -7.87826817\n",
      "  -7.87344029 -7.86723396 -7.86015321 -7.84104271 -7.82307664 -7.8086124\n",
      "  -7.79836343 -7.79175325 -7.78771697 -7.78531972 -7.78391847]]\n",
      "Hartree-Fock energies: [-7.29954105 -7.48594487 -7.61577016 -7.70575334 -7.76736214 -7.80874318\n",
      " -7.83561583 -7.85195386 -7.86053866 -7.86335762 -7.86186477 -7.85714496\n",
      " -7.8500187  -7.84111204 -7.83090558 -7.80193896 -7.77087367 -7.74000074\n",
      " -7.7108299  -7.68437642 -7.6612016  -7.64145387 -7.62497563]\n",
      "VQE num evaluations: [71. 62. 71. 71. 71. 71. 71. 71. 71. 71. 71. 62. 60. 60. 61. 60. 70. 71.\n",
      " 70. 80. 90. 90. 90.]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pylab\n",
    "import copy\n",
    "from qiskit import BasicAer\n",
    "from qiskit.aqua import aqua_globals, QuantumInstance\n",
    "from qiskit.aqua.algorithms import NumPyMinimumEigensolver, VQE\n",
    "from qiskit.aqua.components.optimizers import SLSQP\n",
    "from qiskit.chemistry.components.initial_states import HartreeFock\n",
    "from qiskit.chemistry.components.variational_forms import UCCSD\n",
    "from qiskit.chemistry.drivers import PySCFDriver\n",
    "from qiskit.chemistry.core import Hamiltonian, QubitMappingType\n",
    "\n",
    "molecule = 'H .0 .0 -{0}; Li .0 .0 {0}'\n",
    "algorithms = ['VQE', 'NumPyMinimumEigensolver']\n",
    "\n",
    "pts  = [x * 0.1  for x in range(6, 20)]\n",
    "pts += [x * 0.25 for x in range(8, 16)]\n",
    "pts += [4.0]\n",
    "energies = np.empty([len(algorithms), len(pts)])\n",
    "hf_energies = np.empty(len(pts))\n",
    "distances = np.empty(len(pts))\n",
    "dipoles     = np.empty([len(algorithms), len(pts)])\n",
    "eval_counts = np.empty(len(pts))\n",
    "\n",
    "print('Processing step __', end='')\n",
    "for i, d in enumerate(pts):\n",
    "    print('\\b\\b{:2d}'.format(i), end='', flush=True)\n",
    "    for j in range(len(algorithms)):   \n",
    "        driver = PySCFDriver(molecule.format(d/2), basis='sto3g')\n",
    "        qmolecule = driver.run()\n",
    "        operator =  Hamiltonian(qubit_mapping=QubitMappingType.PARITY,\n",
    "                                two_qubit_reduction=True, freeze_core=True,\n",
    "                                orbital_reduction=[-3, -2])\n",
    "        qubit_op, aux_ops = operator.run(qmolecule)\n",
    "        if algorithms[j] == 'NumPyMinimumEigensolver':\n",
    "            result = NumPyMinimumEigensolver(qubit_op, aux_operators=aux_ops).run()\n",
    "        else:\n",
    "            optimizer = SLSQP(maxiter=1000)\n",
    "            initial_state = HartreeFock(operator.molecule_info['num_orbitals'],\n",
    "                                        operator.molecule_info['num_particles'],\n",
    "                                        qubit_mapping=operator._qubit_mapping,\n",
    "                                        two_qubit_reduction=operator._two_qubit_reduction)\n",
    "            var_form = UCCSD(num_orbitals=operator.molecule_info['num_orbitals'],\n",
    "                            num_particles=operator.molecule_info['num_particles'],\n",
    "                            initial_state=initial_state,\n",
    "                            qubit_mapping=operator._qubit_mapping,\n",
    "                            two_qubit_reduction=operator._two_qubit_reduction)\n",
    "            algo = VQE(qubit_op, var_form, optimizer, aux_operators=aux_ops)\n",
    "            result = algo.run(QuantumInstance(BasicAer.get_backend('statevector_simulator')))\n",
    "            eval_counts[i] = result.optimizer_evals\n",
    "            \n",
    "        result = operator.process_algorithm_result(result)\n",
    "        energies[j][i] = result.energy\n",
    "        hf_energies[i] = result.hartree_fock_energy\n",
    "        dipoles[j][i]  = result.total_dipole_moment / 0.393430307\n",
    "\n",
    "    distances[i] = d\n",
    "print(' --- complete')\n",
    "\n",
    "print('Distances: ', distances)\n",
    "print('Energies:', energies)\n",
    "print('Hartree-Fock energies:', hf_energies)\n",
    "print('VQE num evaluations:', eval_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pylab.plot(distances, hf_energies, label='Hartree-Fock')\n",
    "for j in range(len(algorithms)):\n",
    "    pylab.plot(distances, energies[j], label=algorithms[j])\n",
    "pylab.xlabel('Interatomic distance')\n",
    "pylab.ylabel('Energy')\n",
    "pylab.title('LiH Ground State Energy')\n",
    "pylab.legend(loc='upper right');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pylab.plot(distances, np.subtract(hf_energies, energies[1]), label='Hartree-Fock')\n",
    "pylab.plot(distances, np.subtract(energies[0], energies[1]), label='VQE')\n",
    "pylab.xlabel('Interatomic distance')\n",
    "pylab.ylabel('Energy')\n",
    "pylab.title('Energy difference from NumPyMinimumEigensolver')\n",
    "pylab.legend(loc='upper left');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for j in reversed(range(len(algorithms))):\n",
    "    pylab.plot(distances, dipoles[j], label=algorithms[j])\n",
    "pylab.xlabel('Interatomic distance')\n",
    "pylab.ylabel('Moment in debye')\n",
    "pylab.title('LiH Dipole Moment')\n",
    "pylab.legend(loc='upper right');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pylab.plot(distances, eval_counts, '-o', color=[0.8500, 0.3250, 0.0980], label='VQE')\n",
    "pylab.xlabel('Interatomic distance')\n",
    "pylab.ylabel('Evaluations')\n",
    "pylab.title('VQE number of evaluations')\n",
    "pylab.legend(loc='upper left');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
